Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System
As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/14/6875 |
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| author | Mesfer Al Duhayyim Khalid A. Alissa Fatma S. Alrayes Saud S. Alotaibi ElSayed M. Tag El Din Amgad Atta Abdelmageed Ishfaq Yaseen Abdelwahed Motwakel |
| author_facet | Mesfer Al Duhayyim Khalid A. Alissa Fatma S. Alrayes Saud S. Alotaibi ElSayed M. Tag El Din Amgad Atta Abdelmageed Ishfaq Yaseen Abdelwahed Motwakel |
| author_sort | Mesfer Al Duhayyim |
| collection | DOAJ |
| description | As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and control the physical procedure, commonly with feedback loops whereas physical procedures affect calculations and conversely, at the same time, ML approaches were vulnerable to data pollution attacks. Improving network security and attaining robustness of ML determined network schemes were the critical problems of the growth of CPS. This study develops a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection system (SFSA-DLIDS) for a cloud-based CPS environment. The presented SFSA-DLIDS technique majorly focuses on the recognition and classification of intrusions for accomplishing security from the CPS environment. The presented SFSA-DLIDS approach primarily performs a min-max data normalization approach to convert the input data to a compatible format. In order to reduce a curse of dimensionality, the SFSA technique is applied to select a subset of features. Furthermore, chicken swarm optimization (CSO) with deep stacked auto encoder (DSAE) technique was utilized for the identification and classification of intrusions. The design of a CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model is tested using a series of experiments. The experimental results depict the promising performance of the SFSA-DLIDS model over the recent models. |
| first_indexed | 2024-03-09T03:45:02Z |
| format | Article |
| id | doaj.art-0d1a793e4c7e44868315964eabb76f1b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T03:45:02Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-0d1a793e4c7e44868315964eabb76f1b2023-12-03T14:34:56ZengMDPI AGApplied Sciences2076-34172022-07-011214687510.3390/app12146875Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical SystemMesfer Al Duhayyim0Khalid A. Alissa1Fatma S. Alrayes2Saud S. Alotaibi3ElSayed M. Tag El Din4Amgad Atta Abdelmageed5Ishfaq Yaseen6Abdelwahed Motwakel7Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaSAUDI ARAMCO Cybersecurity Chair, Networks and Communications Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Information Systems, College of Computing and Information System, Umm Al-Qura University, Mecca 24382, Saudi ArabiaDepartment of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11845, EgyptDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaDepartment of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi ArabiaAs cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and control the physical procedure, commonly with feedback loops whereas physical procedures affect calculations and conversely, at the same time, ML approaches were vulnerable to data pollution attacks. Improving network security and attaining robustness of ML determined network schemes were the critical problems of the growth of CPS. This study develops a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection system (SFSA-DLIDS) for a cloud-based CPS environment. The presented SFSA-DLIDS technique majorly focuses on the recognition and classification of intrusions for accomplishing security from the CPS environment. The presented SFSA-DLIDS approach primarily performs a min-max data normalization approach to convert the input data to a compatible format. In order to reduce a curse of dimensionality, the SFSA technique is applied to select a subset of features. Furthermore, chicken swarm optimization (CSO) with deep stacked auto encoder (DSAE) technique was utilized for the identification and classification of intrusions. The design of a CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model is tested using a series of experiments. The experimental results depict the promising performance of the SFSA-DLIDS model over the recent models.https://www.mdpi.com/2076-3417/12/14/6875Internet of Thingsdeep learningcyber physical systemscloud computingintrusion detectionsecurity |
| spellingShingle | Mesfer Al Duhayyim Khalid A. Alissa Fatma S. Alrayes Saud S. Alotaibi ElSayed M. Tag El Din Amgad Atta Abdelmageed Ishfaq Yaseen Abdelwahed Motwakel Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System Applied Sciences Internet of Things deep learning cyber physical systems cloud computing intrusion detection security |
| title | Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System |
| title_full | Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System |
| title_fullStr | Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System |
| title_full_unstemmed | Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System |
| title_short | Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System |
| title_sort | evolutionary based deep stacked autoencoder for intrusion detection in a cloud based cyber physical system |
| topic | Internet of Things deep learning cyber physical systems cloud computing intrusion detection security |
| url | https://www.mdpi.com/2076-3417/12/14/6875 |
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